Machine learning approaches to identifying neurodevelopmental disorders using social media data: a systematic review

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: F1-scores ranging from 0.48 to 0.89

• Primary Outcome: Diagnostic classification performance of machine learning models

• Nnt Or Sensitivity: Video-based models showed particular promise for nonverbal behavioral markers

• Confidence Interval: Not reported

Clinical Application

Limited by ethical concerns, privacy regulations, and need for specialized technical infrastructure Would require compliance with Australian Privacy Principles and TGA regulations for digital health tools; potential integration with existing NDIS assessment pathways Children and adolescents with suspected neurodevelopmental disorders who use social media platforms

Abstract

BACKGROUND: Neurodevelopmental disorders (NDDs), such as autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), often emerge in early childhood and can significantly impact social, academic, and emotional functioning. Early identification is critical to improving long-term outcomes, yet traditional diagnostic processes are time-consuming and resource-intensive. As social media becomes an integral part of daily life, user-generated content offers a novel source of behavioral and linguistic data that may support early detection. Advances in artificial intelligence (AI) and machine learning (ML) now make it possible to analyze these large-scale data streams for clinical insights. METHODS: A comprehensive search was performed across five databases-PubMed, Scopus, Web of Science, IEEE Xplore, and ACM Digital Library-from inception up to July 2025. Studies were included if they used AI or ML methods to analyze social media data for detecting NDDs. Data extraction focused on platform type, targeted disorder, dataset size, ML technique, and diagnostic performance. RESULTS: Nineteen studies met the inclusion criteria. Most focused on ASD and ADHD, using platforms such as Reddit, Twitter, YouTube, and Facebook. ML models achieved moderate to high classification performance, with F1-scores ranging from approximately 0.48 to 0.89 depending on the data type and disorder. Video-based models showed particular promise in identifying nonverbal behavioral markers. However, research on other NDDs remains limited, and methodological heterogeneity, small sample sizes, and ethical challenges persist. CONCLUSION: AI-driven analysis of social media data holds significant promise for scalable, non-invasive screening of neurodevelopmental disorders. While current work largely focuses on ASD and ADHD, future research should extend to underrepresented NDDs and address concerns related to data validity, bias, and privacy. With continued advancement, these tools may serve as valuable complements to traditional diagnostic methods.

References

1. 1.Mohammad, S. R., Kadaei, F., & Mohkam, M. (2026). Machine learning approaches to identifying neurodevelopmental disorders using social media data: a systematic review. International Journal of Medical Informatics, 106378. https://doi.org/10.1016/j.ijmedinf.2026.106378